Forget Nvidia and SpaceX. This 150-Year-Old Indiana Company Could Become the World’s Largest in 5 Years

For most investors, the race to become the world’s most valuable company feels like a contest between Nvidia (NASDAQ:NVDA | NVDA Price Prediction) and a handful of AI-first businesses. Yet a growing number of investors are starting to think the biggest AI winner may be a company emerging from industries already possessing something harder to build than a large language model — proprietary data accumulated over decades.

That is the argument investor Jordi Visser recently laid out on The Pomp Podcast. His view is that the biggest AI winner will be a company in Indiana that has been around since the 19th century: Eli Lilly (NYSE:LLY). The pharmaceutical giant won’t outcompete Nvidia selling chips. Rather, Lilly has a credible path to becoming the world’s largest company within five years because it sits at the intersection of artificial intelligence, proprietary healthcare data, and blockbuster obesity and diabetes treatments.

The AI Infrastructure Story Investors Are Missing

Most investors think of Lilly as a pharma riding the success of GLP-1 drugs such as Mounjaro and Zepbound. Those products have already transformed the company’s financial profile, helping push its market capitalization to $1.08 trillion.

Visser’s thesis goes further. He points to several AI initiatives that make Lilly look less like a traditional drugmaker and more like a large-scale AI application company:

• A private AI infrastructure reportedly built around roughly 1,000 Nvidia Blackwell GPUs.
• A co-innovation relationship with Nvidia and CEO Jensen Huang.
• Partnerships connected to Google’s AlphaFold through Isomorphic Labs.
• A Silicon Valley research presence through its TuneLab initiative.

In isolation, any one of those investments might not be remarkable. Taken together, they suggest Lilly is building a substantial AI capability inside the pharmaceutical business.

Why Data Matters More Than GPUs

Hardware can be purchased and partnerships can be signed, but data is harder.

Visser argues Lilly’s strongest asset is its 150 years of proprietary metabolic disease data, including information related to diabetes, obesity, and other metabolic conditions. That dataset was accumulated through decades of clinical research, patient outcomes, and drug development.

Here’s why that matters: general AI models can be trained on publicly available information, but they cannot simply recreate decades of real-world biological data. In healthcare, the quality and uniqueness of the underlying data often determine how useful an AI system becomes.

For investors, this is the same principle that has historically benefited companies with proprietary customer data, search data, or transaction data. The difference is that Lilly’s data relates to human biology, one of the largest economic markets in the world.

The Category Mismatch

Another part of the thesis is that the market may still be valuing Lilly primarily as a pharmaceutical stock.

Investors generally associate the company with obesity drugs, diabetes treatments, and healthcare spending. Its AI investments are often viewed as supporting tools rather than as a central driver of future value.

Visser believes that framing could change. If investors begin to see Lilly as an AI-powered drug discovery platform with one of the world’s richest metabolic datasets, the valuation framework may shift.

That does not guarantee Lilly becomes the world’s largest company. It does mean the company could be competing in a larger category than many investors currently assign to it.

The Bigger AI Lesson

One of the most interesting aspects of this debate is what it says about AI investing more broadly. The early AI winners have largely been infrastructure providers: chip makers, cloud platforms, and model developers. Over time, the bigger opportunity may shift toward companies that combine AI with unique domain expertise and proprietary data.

Healthcare is a prime candidate. Drug discovery is expensive, time-consuming, and data-intensive. If AI can reduce the cost or increase the success rate of finding new therapies, the economic impact could be enormous. Recent advances in protein modeling, genomics, and clinical trial analysis suggest that possibility is no longer theoretical.

That is why some investors now view healthcare as a potential long-term beneficiary of AI, even if it does not dominate today’s headlines.

Key Takeaway

In short, the argument for Eli Lilly is not that it will suddenly become a software company. It is that AI may dramatically increase the value of the company’s existing strengths: metabolic disease expertise, proprietary clinical data, and a growing portfolio of obesity and diabetes treatments.

Of course, there are risks. Drug development remains uncertain and regulatory challenges remain real. Today’s AI infrastructure can become tomorrow’s commodity. Nvidia may continue to dominate AI hardware, and other technology giants could maintain larger market capitalizations for years.

But the broader point is harder to dismiss: in the AI era, the companies with the most valuable proprietary data may ultimately capture more value than the companies that merely provide the tools. 

Eli Lilly’s 150-year head start in metabolic disease research gives it a moat that is difficult to replicate. Whether that moat is large enough to make it the world’s biggest company remains to be seen, but it explains why some investors are starting to view this old-line Indiana pharmaceutical company as one of the most interesting AI stories on the market.